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Related Questions
- How do word embeddings address the issue of synonyms and polysemy in natural language processing?
- Can you explain the concept of polysemy and how it affects traditional word representations?
- What are the key differences between Word2Vec and GloVe in capturing word relationships and resolving polysemy?
- How do word embeddings handle homographs and their multiple meanings?
- In what ways do word embeddings improve over traditional word representations in handling polysemy?
- Can you provide examples of how word embeddings resolve polysemy in real-world applications?
- How do word embeddings contribute to improving the accuracy of downstream NLP tasks such as question answering and text classification?
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